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Join Coverage Analytics — Trailing Mean Series + Date-by-Category Pivot
x402
Join Coverage Analytics — Trailing Mean Series + Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a trailing mean time series and a date-by-category pivot table for supplied join coverage metrics (matched_rows, source_rows) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot for supplied join coverage records (matched_rows, source_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a trailing mean time series for the selected metric (matched_rows or source_rows) over the specified rolling window, and (2) a pivot table cross-tabulating values by date and dataset_group category — all computed from the supplied rows without any external lookup.
Example use cases
ETL pipeline join quality monitoring
“I have daily join coverage stats for my ETL pipeline across two dataset groups over the past week — can you compute a trailing mean series with window 3 on matched_rows and pivot the results by date and dataset group so I can see where match rates are dropping?”
Data ingestion coverage trending
“Here's my join coverage table with source_rows and matched_rows for groups A and B from October 1–4 — run the trailing mean on source_rows with a window of 2 and give me the date-by-category breakdown as well.”
Weekly data reconciliation report
“I need a quick summary of our weekly join coverage metrics — I'll supply 20 rows with date, dataset_group, matched_rows and source_rows. Please compute the trailing mean series (window 4) on matched_rows and pivot by date and category so I can share it with my data team.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-152?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular join coverage data (matched_rows, source_rows) with date and category dimensions and need both a rolling/trailing mean time series and a date-by-category pivot in a single paid call. Ideal for data engineers and analysts monitoring ETL pipeline join rates across multiple dataset groups over time, without needing external data or predictions.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside 2–200 range — validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema error
- Non-numeric values in matched_rows or source_rows — type error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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